ESG data integration
in financial services
Abstract
In recent times, environmental, social, and governance (ESG) data has started playing a crucial role in the banking and financial services industry. While ESG data is essential in risk assessment and performance analysis, financial institutions face critical challenges in integrating ESG data into operations. The difficulties include data availability, data quality, data source quality, agnostic presentation of performance, and data governance.
What makes the integration more complex and intriguing is that present-day ESG reporting is often suggestive and largely pursued to improve brand image rather than being a mandatory practice. With increased enforcement of social accountability in the industry, reporting is yet to be standardized, despite various standards and formats already present. This paper highlights the data challenges that entities face in assessing ESG performance, integrating the same into the mainstream operations, and the role that technology can play in closing the gaps.
ESG integration into operations and business strategy
Finance drives economic growth, but improved incomes and returns at the expense of sustainability are catastrophic. In fact, positive environmental, social, and governance (ESG) performance has a beneficial effect on corporate financial performance (CFP) through the elimination of penalties and compliance issues that affect brand equity. Renewed focus on ESG investing and lending, green bonds, green real estate, green mortgages, green trade finance, and sustainability-linked loans improves ESG performance.
Global regulations on ESG such as Sustainable Finance Disclosure Regulation (SFDR), EU taxonomy alignment, and Task Force on Climate-related Financial Disclosures (TCFD) for non-financial information are slowly becoming mandatory. Further, the upcoming directive on mandatory reporting for the Corporate Sustainability Reporting Directive (CSRD) will demand more robust sustainability reporting standards from companies than what the Non-Financial Reporting Directive (NFRD) currently requires. Consequently, investors, banks, and insurers are focusing on being ESG compliant now, more than ever.
Many companies and countries talk about their sustainability and net-zero strategies on their website and other publications. Lately, even end-consumers and individual investors have become
‘sustainability natives’ when it comes to their social, financial, or consumption habits. Thus, institutional financial investors have little option but to become ESG-conscious while investing
enticing investor reports, and colorful web pages. The same is the case with banks in lending, asset management, green financing, and circular financing. Given these rising sustainability aspirations, ESG reporting, disclosures, and associated data needs are also undergoing a transformation.
Searching for the proverbial needle in the haystack
A critical, unbiased, and transparent view of ESG performance is a necessity in order to present a long-term view and avoid misconceptions of deception and greenwashing. But the difficulty in achieving this is that ESG performance in the commercial sphere has never been prescriptive. It has always been an incidental outcome of efforts, voluntarily pursued and disclosed. The reported sustainability impact is highly subjective, with performance data submerged in glossy text-laden reports that are open to interpretations and have less traceability. The absence of quantifiable and standard data further poses challenges of comparability, agnostics, and aggregation difficulties in terms of:
• Tackling non-performance in ESG
• Formulating sustainable strategies and plans for the future
• Giving the true picture to investors for investing
• Managing risks effectively
An increase in new reporting dimensions and a plethora of agencies in the fray have only complicated the domain. Rating agencies use proprietary and hardly comparable methods to formulate ESG scores, resulting in apples to oranges comparisons while investing in funds, assessing lending
proposals, and understanding the quality and ESG risk of assets. Data is either piecemeal in its lowest granularity or wholesome (aggregate or pillar level score). Today, bankers, investment analysts, and underwriters are interested in getting a custom handle on the ESG information of clients, customers, companies, and suppliers. Their focus is to analyze the data with their private risk parameters and analytical dimensions limited to their set of client profiles rather than getting bulk third-party ESG data topped with analytics bias. The following aspects further amplify the challenges:
Disparate data sources
The data sources of ESG information are self-published reports of companies, commercial or
subscribed data from data aggregators, rating agencies, other industry and regulatory organizations, and social media. The primary need is a defined set of criteria to understand the authenticity and qualify the data, especially, as at times, data even from a circumspect source can bring down the credibility in data in investment decisions, asset management, and underwriting.
Poor data quality
The data formats range from quantitative reports to qualitative commentaries, making it non- homogeneous in terms of representations, units of measurements, and methodologies adopted in derivations. Data is highly relative but not always absolute. Companies often report data that is not time-synchronized to compare within their operations or with peers. ESG data feeds from social media further complicate the situation. Furthermore, data is not available from private companies, which form a sizeable clientele of financial institutions. This calls for the abstraction of data from macro-industry statistics, peer comparison, and the like. Without high-quality and authentic ESG data, decisions on lending, investment, underwriting, and reporting are bound to be defective, resulting in business losses, lack of opportunities, non-compliance, and dent in brand equity.
Not-agnostic
Third parties use proprietary algorithms to procure ESG data. Hence, it comes with an inherent
‘proprietary analyst bias’, which may differ from the analytical perspective of financial institutions that use this data. As the institutional strategies and perspectives change, there is a desperate need for subject matter experts to use granular raw data. The availability of an enabling platform powered with raw data provides enormous flexibility in the hands of research analysts and ESG experts. The platform would be suggestive on standard indicators in addition to building custom indicators on ESG performance.
Data is expensive
ESG data is available at a cost. In addition to a license fee, it comes with fixed conditions in terms of usage and distribution. Indiscriminate use of ESG data within financial institutions may result in substantial costs, which is why there are usage, storage, distribution, and geo-specific restrictions in place.
Making meaning out of the madness
Investment analysts, managers, and financial impact analyzers can synchronize the data available through internal insights, external rating agencies, and real-time feeds to take meaningful decisions.
Without this, financial institutions defaulting in the decarbonization trajectory may face penalties and see themselves out of business in the future. Reporting ESG information and integrating it into mainstream operations involves specific data sources, standardized data formats, and essential solution components (see Figure 1). To manage ESG data, financial institutions must:
• Implement an internal insights solution to handle granular preliminary ESG data and supplement third-party data and real-time ESG data feed with a 360-degree view of ESG performance. This necessitates a maneuverable ESG integration solution with data sources assimilation, data ingestion, storage, processing, quality management of both source and data, consumption and visualization, and governance.
• Implement seamless data ingestion from open data sources, company websites, published reports, sustainability data providers through judicious use of artificial intelligence (AI), machine learning (ML) techniques, and intelligent document extractions. Wherever operations are in the cloud, capitalizing on the gateways, marketplaces, and analytics hubs from cloud service providers will streamline the data acquisition and governance.
• Procure data from all ecosystem players, such as traditional and paid ESG data providers, through APIs, which can enhance the richness of data. Since the data is ill-structured with gaps, it requires advanced tools and techniques for unobtrusive acquisition. AI and ML techniques are probable options for acquiring data from free text, graphs, tables, and infographics.
• Perform sentiment analysis to compare real-time ESG performance with past disclosures.
• Incorporate standardization based on most common metrics and missing data, bolstered by robust data logic.
• Enforce quality management (authenticity of sources, levels of abstraction, and data quality) to get near-perfect data.
• Manage source quality through assessment tools.
Such a cascading approach to assessing the ESG performance of companies will strengthen the hands of investment bankers and asset managers in delivering key business impacts, increasing brand equity
Conclusion
clearly state how they will incorporate sustainability mandates into their management strategy to allow consumers to make informed judgments. Making sustainable investments begins with
Figure 1: ESG data play
Graphs Textual
Statements Infographics Tabular Data
Sector and region level ESG data sources
Subscribed ESG data sources
Open ESG data sources ESG news and controversies
Yes
No Level of data
Source of data
Data dump
Data processing
AI- and ML-powered document handler
Associa on or industry level reports, publica ons, or peer group companies'
annual reports Company published reports and disclosures
API handler
Data Representaons
quality managementData source
Data governance
Data quality scoring
quality scoreData performance ESG
Abstract to company level Pick the peer group
average or sector level ESG data Is company
level data available?
•
the data pipeline.
•
About the authors
Subramanian Kuppuswami
Subramanian Kuppuswami (Subi) is the global head of Sustainable Banking and Investments in the Banking, Financial Services, and Insurance (BFSI) business unit at TCS. Subi has over 25 years of sales, solution development, and technology experience in banking and financial services. With a passion for sustainability, Subi enjoys working at the intersection of sustainability and ESG, business and technology, championing TCS’ efforts in externalizing its sustainability capabilities and crafting differentiated solutions to address the rapidly emerging needs in this space. Prior to this, Subi was responsible for leading large sales opportunities for multiple TCS clients across Europe and the UK.
Subi holds a bachelor’s degree in Electrical and Electronics Engineering and is a sports enthusiast who loves playing cricket, badminton, squash, and enjoys coaching young kids in cricket.
M Indira Priyadarsini
M Indira Priyadarsini (Indu) heads Solution and Delivery in the Sustainable Banking and Investments group in the Banking, Financial Services, and Insurance (BFSI) business unit at TCS. She has over 30 years of industry experience working in development banking, public service delivery, and sustainability programs for both private and public sector clients. Indu has also played leadership roles in consulting, solution delivery, business development, relationship management, and in developing solution frameworks for social sectors and sustainability (environmental, social, and governance). She specializes in impact tracking, performance management of public programs, social projects, and ESG initiatives, in addition to tracking carbon footprint in sustainable operations and business of financial institutions with various solution alternatives. Indu holds a bachelor’s degree
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